{"id":"W4306317452","doi":"10.1145/3511808.3557209","title":"Named Entity-based Question-Answering Pair Generator","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Pipeline (software); Paragraph; Question answering; Generator (circuit theory); Task (project management); Context (archaeology); Abstraction; Simple (philosophy); Text generation; Natural language processing; Argument (complex analysis); Artificial intelligence; Programming language; Engineering; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005649142,0.001835522,0.001036522,0.001978004,0.0007890068,0.001408177,0.003180976,0.002171846,0.02326245],"category_scores_gemma":[0.0170965,0.0008507947,0.001694785,0.001183533,0.000887506,0.003598821,0.00406397,0.001860701,0.009585091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008689616,"about_ca_system_score_gemma":0.001088651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00107191,"about_ca_topic_score_gemma":0.0009091084,"domain_scores_codex":[0.995666,0.002139225,0.0002746223,0.000994851,0.0007197476,0.0002054646],"domain_scores_gemma":[0.990913,0.005523609,0.0002582547,0.001609533,0.001450934,0.0002446769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00218847,0.0007497321,0.005234408,0.001997889,0.0002931121,0.002462802,0.003276229,0.03623043,0.07024956,0.1217833,0.115495,0.6400391],"study_design_scores_gemma":[0.0004406049,0.0005180723,0.001397318,0.0001076959,0.0001756963,0.001304006,0.0006870131,0.6634192,0.1041707,0.1218334,0.1057748,0.0001714959],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004617751,0.00009459643,0.9700353,0.0002953246,0.0001280989,0.0005855726,0.001446048,0.0207014,0.002095946],"genre_scores_gemma":[0.1394062,0.0001090102,0.8399238,0.0004254933,0.0001192851,0.001443091,0.009396971,0.00290194,0.006274046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02326245,"threshold_uncertainty_score":0.0778206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04509539980294194,"score_gpt":0.2833785768154452,"score_spread":0.2382831770125033,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}